Overview

Dataset statistics

Number of variables15
Number of observations6497
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.5 KiB
Average record size in memory120.0 B

Variable types

Categorical1
Numeric14

Alerts

fixed_acidity is highly overall correlated with typeHigh correlation
volatile_acidity is highly overall correlated with typeHigh correlation
residual_sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free_sulfur_dioxide is highly overall correlated with total_sulfur_dioxideHigh correlation
total_sulfur_dioxide is highly overall correlated with free_sulfur_dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual_sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
type is highly overall correlated with fixed_acidity and 3 other fieldsHigh correlation
citric_acid has 151 (2.3%) zerosZeros

Reproduction

Analysis started2023-08-15 15:22:18.579734
Analysis finished2023-08-15 15:22:44.043960
Duration25.46 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
white
4898 
red
1599 

Length

Max length5
Median length5
Mean length4.5077728
Min length3

Characters and Unicode

Total characters29287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowred
3rd rowred
4th rowred
5th rowred

Common Values

ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Length

2023-08-15T16:22:44.159004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-15T16:22:44.353953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Most occurring characters

ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29287
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 29287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

fixed_acidity
Real number (ℝ)

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2153071
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:44.461509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2964338
Coefficient of variation (CV)0.17967825
Kurtosis5.0611607
Mean7.2153071
Median Absolute Deviation (MAD)0.6
Skewness1.7232896
Sum46877.85
Variance1.6807405
MonotonicityNot monotonic
2023-08-15T16:22:44.589486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 354
 
5.4%
6.6 327
 
5.0%
6.4 305
 
4.7%
7 282
 
4.3%
6.9 279
 
4.3%
7.2 273
 
4.2%
6.7 264
 
4.1%
7.1 257
 
4.0%
6.5 242
 
3.7%
7.4 238
 
3.7%
Other values (96) 3676
56.6%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
< 0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.1%
4.9 8
 
0.1%
5 30
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile_acidity
Real number (ℝ)

Distinct187
Distinct (%)2.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.33964132
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:44.711165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16463713
Coefficient of variation (CV)0.48473821
Kurtosis2.8269365
Mean0.33964132
Median Absolute Deviation (MAD)0.08
Skewness1.4956167
Sum2206.31
Variance0.027105383
MonotonicityNot monotonic
2023-08-15T16:22:44.830616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 286
 
4.4%
0.24 266
 
4.1%
0.26 256
 
3.9%
0.25 238
 
3.7%
0.22 235
 
3.6%
0.27 232
 
3.6%
0.23 221
 
3.4%
0.2 217
 
3.3%
0.3 214
 
3.3%
0.32 205
 
3.2%
Other values (177) 4126
63.5%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.2%
0.115 3
 
< 0.1%
0.12 37
0.6%
0.125 3
 
< 0.1%
0.13 44
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric_acid
Real number (ℝ)

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31863322
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:44.968216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.14531786
Coefficient of variation (CV)0.45606628
Kurtosis2.3972392
Mean0.31863322
Median Absolute Deviation (MAD)0.07
Skewness0.47173067
Sum2070.16
Variance0.021117282
MonotonicityNot monotonic
2023-08-15T16:22:45.083376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 337
 
5.2%
0.28 301
 
4.6%
0.32 289
 
4.4%
0.49 283
 
4.4%
0.26 257
 
4.0%
0.34 249
 
3.8%
0.29 244
 
3.8%
0.27 236
 
3.6%
0.24 232
 
3.6%
0.31 230
 
3.5%
Other values (79) 3839
59.1%
ValueCountFrequency (%)
0 151
2.3%
0.01 40
 
0.6%
0.02 56
 
0.9%
0.03 32
 
0.5%
0.04 41
 
0.6%
0.05 25
 
0.4%
0.06 30
 
0.5%
0.07 34
 
0.5%
0.08 37
 
0.6%
0.09 42
 
0.6%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual_sugar
Real number (ℝ)

Distinct316
Distinct (%)4.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.4437192
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:45.228271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7580101
Coefficient of variation (CV)0.87403665
Kurtosis4.3585153
Mean5.4437192
Median Absolute Deviation (MAD)1.7
Skewness1.4351777
Sum35362.4
Variance22.63866
MonotonicityNot monotonic
2023-08-15T16:22:45.348720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235
 
3.6%
1.8 228
 
3.5%
1.6 223
 
3.4%
1.4 219
 
3.4%
1.2 195
 
3.0%
2.2 187
 
2.9%
2.1 179
 
2.8%
1.9 176
 
2.7%
1.7 175
 
2.7%
1.5 172
 
2.6%
Other values (306) 4507
69.4%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.4%
0.9 41
 
0.6%
0.95 4
 
0.1%
1 93
1.4%
1.05 1
 
< 0.1%
1.1 146
2.2%
1.15 3
 
< 0.1%
1.2 195
3.0%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

Distinct214
Distinct (%)3.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.056034945
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:45.485331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.03503619
Coefficient of variation (CV)0.62525607
Kurtosis50.889762
Mean0.056034945
Median Absolute Deviation (MAD)0.011
Skewness5.3993711
Sum364.003
Variance0.0012275346
MonotonicityNot monotonic
2023-08-15T16:22:45.606512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 206
 
3.2%
0.036 200
 
3.1%
0.042 187
 
2.9%
0.046 185
 
2.8%
0.04 182
 
2.8%
0.05 182
 
2.8%
0.048 182
 
2.8%
0.047 175
 
2.7%
0.045 174
 
2.7%
0.038 169
 
2.6%
Other values (204) 4654
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.1%
0.02 16
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
< 0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free_sulfur_dioxide
Real number (ℝ)

Distinct135
Distinct (%)2.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.528787
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:45.746086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.748565
Coefficient of variation (CV)0.58137145
Kurtosis7.9086168
Mean30.528787
Median Absolute Deviation (MAD)12
Skewness1.2201547
Sum198315
Variance315.01157
MonotonicityNot monotonic
2023-08-15T16:22:45.892966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 183
 
2.8%
6 170
 
2.6%
26 161
 
2.5%
15 157
 
2.4%
24 152
 
2.3%
31 152
 
2.3%
17 149
 
2.3%
34 146
 
2.2%
35 144
 
2.2%
23 142
 
2.2%
Other values (125) 4940
76.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.9%
4 52
 
0.8%
5 129
2.0%
5.5 1
 
< 0.1%
6 170
2.6%
7 96
1.5%
8 90
1.4%
9 91
1.4%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total_sulfur_dioxide
Real number (ℝ)

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.74457
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:46.038947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.521855
Coefficient of variation (CV)0.48833265
Kurtosis-0.37166365
Mean115.74457
Median Absolute Deviation (MAD)39
Skewness-0.0011774782
Sum751992.5
Variance3194.72
MonotonicityNot monotonic
2023-08-15T16:22:46.176414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 72
 
1.1%
113 65
 
1.0%
117 57
 
0.9%
122 57
 
0.9%
128 56
 
0.9%
98 56
 
0.9%
124 56
 
0.9%
114 56
 
0.9%
118 55
 
0.8%
150 54
 
0.8%
Other values (266) 5913
91.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 4
 
0.1%
8 14
 
0.2%
9 15
0.2%
10 28
0.4%
11 26
0.4%
12 29
0.4%
13 28
0.4%
14 33
0.5%
15 35
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

Distinct998
Distinct (%)15.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.99469616
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:46.304571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999393
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.0029989891
Coefficient of variation (CV)0.0030149801
Kurtosis6.6052524
Mean0.99469616
Median Absolute Deviation (MAD)0.00231
Skewness0.50401094
Sum6460.5515
Variance8.9939356 × 10-6
MonotonicityNot monotonic
2023-08-15T16:22:46.485473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 69
 
1.1%
0.9976 69
 
1.1%
0.992 64
 
1.0%
0.998 64
 
1.0%
0.9928 63
 
1.0%
0.9986 61
 
0.9%
0.9966 59
 
0.9%
0.9962 59
 
0.9%
0.9968 55
 
0.8%
0.9932 54
 
0.8%
Other values (988) 5878
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
 
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
 
< 0.1%
1.00315 3
< 0.1%
1.00295 2
< 0.1%
1.00289 1
 
< 0.1%
1.0026 2
< 0.1%
1.00242 2
< 0.1%
1.00241 1
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2185008
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:46.615972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872
Coefficient of variation (CV)0.049957173
Kurtosis0.36765727
Mean3.2185008
Median Absolute Deviation (MAD)0.11
Skewness0.3868388
Sum20910.6
Variance0.025852524
MonotonicityNot monotonic
2023-08-15T16:22:46.752493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 200
 
3.1%
3.14 193
 
3.0%
3.22 185
 
2.8%
3.2 176
 
2.7%
3.15 170
 
2.6%
3.19 170
 
2.6%
3.18 168
 
2.6%
3.24 161
 
2.5%
3.1 154
 
2.4%
3.12 154
 
2.4%
Other values (98) 4766
73.4%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
< 0.1%
2.8 3
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.84 1
 
< 0.1%
2.85 9
0.1%
2.86 10
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53126828
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:46.892474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14880587
Coefficient of variation (CV)0.28009554
Kurtosis8.6536988
Mean0.53126828
Median Absolute Deviation (MAD)0.08
Skewness1.79727
Sum3451.65
Variance0.022143188
MonotonicityNot monotonic
2023-08-15T16:22:47.012977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 276
 
4.2%
0.46 243
 
3.7%
0.54 235
 
3.6%
0.44 232
 
3.6%
0.38 214
 
3.3%
0.48 208
 
3.2%
0.52 203
 
3.1%
0.49 197
 
3.0%
0.47 191
 
2.9%
0.45 190
 
2.9%
Other values (101) 4308
66.3%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.2%
0.28 13
 
0.2%
0.29 16
 
0.2%
0.3 31
0.5%
0.31 35
0.5%
0.32 54
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
< 0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.491801
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:47.134915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1927117
Coefficient of variation (CV)0.11368037
Kurtosis-0.53168738
Mean10.491801
Median Absolute Deviation (MAD)0.9
Skewness0.56571773
Sum68165.23
Variance1.4225613
MonotonicityNot monotonic
2023-08-15T16:22:47.264462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 367
 
5.6%
9.4 332
 
5.1%
9.2 271
 
4.2%
10 229
 
3.5%
10.5 227
 
3.5%
11 217
 
3.3%
9 215
 
3.3%
9.8 214
 
3.3%
10.4 194
 
3.0%
9.3 193
 
3.0%
Other values (101) 4038
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 5
 
0.1%
8.5 10
 
0.2%
8.6 23
 
0.4%
8.7 80
 
1.2%
8.8 109
1.7%
8.9 95
1.5%
9 215
3.3%
9.05 1
 
< 0.1%
9.1 167
2.6%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 12
0.2%
13.9 3
 
< 0.1%
13.8 2
 
< 0.1%
13.7 7
0.1%
13.6 13
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

X
Real number (ℝ)

Distinct6496
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-20608.952
Minimum-57235.222
Maximum24398.887
Zeros0
Zeros (%)0.0%
Negative5551
Negative (%)85.4%
Memory size50.9 KiB
2023-08-15T16:22:47.401978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-57235.222
5-th percentile-43675.706
Q1-32423.819
median-23464.037
Q3-11658.3
95-th percentile12787.44
Maximum24398.887
Range81634.11
Interquartile range (IQR)20765.518

Descriptive statistics

Standard deviation16921.859
Coefficient of variation (CV)-0.82109265
Kurtosis-0.21420898
Mean-20608.952
Median Absolute Deviation (MAD)10114.994
Skewness0.62786708
Sum-1.3389636 × 108
Variance2.8634931 × 108
MonotonicityNot monotonic
2023-08-15T16:22:47.531823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-19058.15438 2
 
< 0.1%
-11766.86327 1
 
< 0.1%
-18856.40612 1
 
< 0.1%
-41512.91109 1
 
< 0.1%
-47383.75892 1
 
< 0.1%
-31252.8151 1
 
< 0.1%
-26646.40133 1
 
< 0.1%
-30097.46155 1
 
< 0.1%
-12100.75239 1
 
< 0.1%
-28211.88617 1
 
< 0.1%
Other values (6486) 6486
99.8%
ValueCountFrequency (%)
-57235.22232 1
< 0.1%
-56859.63272 1
< 0.1%
-56784.81878 1
< 0.1%
-56731.90936 1
< 0.1%
-56677.59912 1
< 0.1%
-56536.00171 1
< 0.1%
-56212.35443 1
< 0.1%
-56165.09116 1
< 0.1%
-56150.07537 1
< 0.1%
-55227.25914 1
< 0.1%
ValueCountFrequency (%)
24398.88749 1
< 0.1%
24286.70269 1
< 0.1%
24007.26476 1
< 0.1%
24003.6202 1
< 0.1%
23947.52162 1
< 0.1%
23872.28419 1
< 0.1%
21889.82574 1
< 0.1%
21839.40955 1
< 0.1%
21834.98425 1
< 0.1%
21795.13304 1
< 0.1%

Y
Real number (ℝ)

Distinct6496
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222969.41
Minimum183732.7
Maximum274739.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:47.677619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum183732.7
5-th percentile189692.92
Q1200374.76
median217804.87
Q3238683.12
95-th percentile267715.55
Maximum274739.51
Range91006.815
Interquartile range (IQR)38308.358

Descriptive statistics

Standard deviation26345.636
Coefficient of variation (CV)0.11815807
Kurtosis-1.0686507
Mean222969.41
Median Absolute Deviation (MAD)19190.449
Skewness0.48054088
Sum1.4486322 × 109
Variance6.9409252 × 108
MonotonicityNot monotonic
2023-08-15T16:22:47.808319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216174.4645 2
 
< 0.1%
271295.5976 1
 
< 0.1%
190350.5149 1
 
< 0.1%
263835.9381 1
 
< 0.1%
209699.3084 1
 
< 0.1%
195370.757 1
 
< 0.1%
237036.3964 1
 
< 0.1%
192602.8595 1
 
< 0.1%
188208.4296 1
 
< 0.1%
200920.0338 1
 
< 0.1%
Other values (6486) 6486
99.8%
ValueCountFrequency (%)
183732.6954 1
< 0.1%
183754.4166 1
< 0.1%
185003.306 1
< 0.1%
185007.0439 1
< 0.1%
185035.063 1
< 0.1%
185041.9374 1
< 0.1%
185110.1329 1
< 0.1%
185110.5716 1
< 0.1%
185121.2261 1
< 0.1%
185165.5791 1
< 0.1%
ValueCountFrequency (%)
274739.5103 1
< 0.1%
274715.965 1
< 0.1%
274631.2637 1
< 0.1%
274594.3099 1
< 0.1%
274589.991 1
< 0.1%
274565.1681 1
< 0.1%
274394.8947 1
< 0.1%
274328.0724 1
< 0.1%
274313.7037 1
< 0.1%
274273.2434 1
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8183777
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-08-15T16:22:47.905303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87325527
Coefficient of variation (CV)0.1500857
Kurtosis0.23232227
Mean5.8183777
Median Absolute Deviation (MAD)1
Skewness0.18962269
Sum37802
Variance0.76257477
MonotonicityNot monotonic
2023-08-15T16:22:47.986845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2836
43.7%
5 2138
32.9%
7 1079
 
16.6%
4 216
 
3.3%
8 193
 
3.0%
3 30
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 30
 
0.5%
4 216
 
3.3%
5 2138
32.9%
6 2836
43.7%
7 1079
 
16.6%
8 193
 
3.0%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 193
 
3.0%
7 1079
 
16.6%
6 2836
43.7%
5 2138
32.9%
4 216
 
3.3%
3 30
 
0.5%

Interactions

2023-08-15T16:22:41.610532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:19.356535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.216662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.002207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.908484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.629048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.241935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.094195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.636651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.306125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.894978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.706599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.346724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.112146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.723799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:19.537851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.328969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.116094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.022297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.735480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.380905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.206584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.757625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.419771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.008829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.819308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.492335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.217622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.836104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:19.668692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.465695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.244360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.153380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.848940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.486550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.319424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.878364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.534934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.129640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.925279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.612399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.330149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.973896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:19.790676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.612863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.350555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.281456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.962364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.601167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.431939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.990989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.664127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.251283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.037700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.735146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.437296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.079688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:19.903144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.735102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.454857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.411181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.067750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.706387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.537216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.103416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.776527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.552482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.151952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.863074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.541591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.192009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.136868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.855810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.575550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.524892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.180981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.820322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.651055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.215672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.888595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.666667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.272201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.984056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.653510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.304245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.249441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.968279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.688965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.637330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.311200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.961121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.747630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.353829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.009864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.786663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.386180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.105105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.750677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.410395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.345867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.073970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.785304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.750067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.417008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.056241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.844382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.491512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.113868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:35.899293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.492679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.217952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.838673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.531527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.458447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.237049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:23.930549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:25.878066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.512940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.328879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:30.948656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.595513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.218932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.003379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.606003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.332323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:40.951446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.643796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.562451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.374681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.166600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.006141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.618403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.474808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.044863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.717235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.339438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.117165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.734468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.454547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.055678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.772952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.677722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.504300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.313532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.118766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.748539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.645280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.199294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.829817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.453530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.238913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.880704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.634482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.169057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:42.885054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.812504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.629101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.435022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.225911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:27.894624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.773949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.322034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:32.975382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.566075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.360660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:37.993162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.747793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.290534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:43.239306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:20.974863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.745361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.582344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.345940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.014542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.893950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.434561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.095564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.686069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.481153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.105523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.878036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.410534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:43.337445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:21.094862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:22.882202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:24.753977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:26.515891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:28.111950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:29.989949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:31.540324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:33.193600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:34.782343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:36.593532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:38.210728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:39.991763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-15T16:22:41.506615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-08-15T16:22:48.111502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholXYqualitytype
fixed_acidity1.0000.2000.271-0.0320.356-0.260-0.2330.434-0.2500.220-0.111-0.0030.003-0.0980.504
volatile_acidity0.2001.000-0.295-0.0640.416-0.365-0.3440.2610.1950.255-0.024-0.003-0.015-0.2580.664
citric_acid0.271-0.2951.0000.075-0.0740.1220.1590.066-0.2860.0370.0200.0030.0030.1060.424
residual_sugar-0.032-0.0640.0751.000-0.0360.3880.4550.527-0.229-0.138-0.3290.0180.009-0.0170.350
chlorides0.3560.416-0.074-0.0361.000-0.260-0.2680.5910.1640.370-0.401-0.010-0.009-0.2950.765
free_sulfur_dioxide-0.260-0.3650.1220.388-0.2601.0000.7410.006-0.165-0.221-0.186-0.0100.0160.0870.419
total_sulfur_dioxide-0.233-0.3440.1590.455-0.2680.7411.0000.062-0.243-0.257-0.309-0.0030.017-0.0550.800
density0.4340.2610.0660.5270.5910.0060.0621.0000.0120.275-0.6990.0060.000-0.3230.322
pH-0.2500.195-0.286-0.2290.164-0.165-0.2430.0121.0000.2540.1400.000-0.0040.0330.333
sulphates0.2200.2550.037-0.1380.370-0.221-0.2570.2750.2541.0000.005-0.017-0.0080.0300.472
alcohol-0.111-0.0240.020-0.329-0.401-0.186-0.309-0.6990.1400.0051.000-0.0150.0010.4470.147
X-0.003-0.0030.0030.018-0.010-0.010-0.0030.0060.000-0.017-0.0151.000-0.342-0.0040.028
Y0.003-0.0150.0030.009-0.0090.0160.0170.000-0.004-0.0080.001-0.3421.0000.0010.036
quality-0.098-0.2580.106-0.017-0.2950.087-0.055-0.3230.0330.0300.447-0.0040.0011.0000.130
type0.5040.6640.4240.3500.7650.4190.8000.3220.3330.4720.1470.0280.0360.1301.000

Missing values

2023-08-15T16:22:43.516345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-15T16:22:43.767631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-15T16:22:43.945531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

typefixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholXYquality
0red7.40.700.001.90.07611.034.00.99783.510.569.4-5133.037065274269.6612205
1red7.80.880.002.60.09825.067.00.99683.200.689.8-19891.404070200352.2261645
2red7.80.760.042.30.09215.054.00.99703.260.659.8-20344.634864223184.2429985
3red11.20.280.561.90.07517.060.00.99803.160.589.8-39206.746544233633.2490376
4red7.40.700.001.90.07611.034.00.99783.510.569.4-30277.806444223382.3323075
5red7.40.660.001.80.07513.040.00.99783.510.569.4-3479.217602202207.4945035
6red7.90.600.061.60.06915.059.00.99643.300.469.4-33852.294526213748.6276335
7red7.30.650.001.20.06515.021.00.99463.390.4710.0-12766.093599199255.6873377
8red7.80.580.022.00.0739.018.00.99683.360.579.5-23069.954957201810.8541447
9red7.50.500.366.10.07117.0102.00.99783.350.8010.5-28100.555582262625.2363235
typefixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholXYquality
6487white6.80.2200.361.200.05238.0127.00.993303.040.549.29061.449890204050.2964955
6488white4.90.2350.2711.750.03034.0118.00.995403.070.509.4-24684.446928264507.1231446
6489white6.10.3400.292.200.03625.0100.00.989383.060.4411.8-35246.119620261653.9375546
6490white5.70.2100.320.900.03838.0121.00.990743.240.4610.6-27925.720992189457.0296786
6491white6.50.2300.381.300.03229.0112.00.992983.290.549.7-26631.268051198739.7578885
6492white6.20.2100.291.600.03924.092.00.991143.270.5011.2-11906.686119220458.4247156
6493white6.60.3200.368.000.04757.0168.00.994903.150.469.6-40305.138001222811.8097855
6494white6.50.2400.191.200.04130.0111.00.992542.990.469.4-20915.489754216220.3239416
6495white5.50.2900.301.100.02220.0110.00.988693.340.3812.8-28367.071006194490.8843437
6496white6.00.2100.380.800.02022.098.00.989413.260.3211.8-16086.895896200685.4869366